Name
Title | ||
|---|---|---|
Diagnosis and Recognition of Grape Leaf Diseases: An automated system based on a Novel Saliency approach and Canonical Correlation Analysis based multiple features fusion |
Abstract | ||
|---|---|---|
•Low contrast haze reduction approach is proposed for contrast enhancement and noise removal.•A thresholding function is defined for segmentation of disease region where LAB image is utilized as input.•Canonical correlation-based features fusion is performed.•NCA based irrelevant features are reduced.•A disease-based fair comparison is conducted at the end. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.suscom.2019.08.002 | Sustainable Computing: Informatics and Systems |
Keywords | Field | DocType |
Fruit diseases,Contrast stretching,Saliency estimation,Features fusion,Reduction,Recognition | Pattern recognition,Salience (neuroscience),Segmentation,Canonical correlation,Computer science,Support vector machine,Fusion,Image processing,Artificial intelligence,Pixel,Thresholding | Journal |
Volume | ISSN | Citations |
24 | 2210-5379 | 3 |
PageRank | References | Authors |
0.46 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alishba Adeel | 1 | 3 | 0.46 |
| Muhammad Attique Khan | 2 | 47 | 9.72 |
| Muhammad Sharif | 3 | 317 | 37.96 |
| Faisal Azam | 4 | 3 | 0.46 |
| Tariq Umer | 5 | 116 | 15.42 |
| Shaohua Wan | 6 | 382 | 48.34 |